{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# 创建Tensor\n",
    "- 演示各种Tensor的创建方式"
   ],
   "id": "e400ae9dbf3ceb67"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T06:55:14.306284Z",
     "start_time": "2025-11-04T06:55:14.291209Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "# 指定固定的值\n",
    "a = torch.tensor([[1,2,3],[4,5,6],[7,8,9]], dtype=torch.float32, device='cuda:0')\n",
    "print(a)\n",
    "print(a.type())"
   ],
   "id": "dc0df80c78be85f2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 2., 3.],\n",
      "        [4., 5., 6.],\n",
      "        [7., 8., 9.]], device='cuda:0')\n",
      "torch.cuda.FloatTensor\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T07:02:18.149951Z",
     "start_time": "2025-11-04T07:02:18.145720Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "\n",
    "a = torch.Tensor(2,3)\n",
    "\n",
    "print(a)\n",
    "print(a.size())\n",
    "print(a.type())\n"
   ],
   "id": "259a47ba4c249dc3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.6341e+29,  1.4293e-42,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00,  0.0000e+00]])\n",
      "torch.Size([2, 3])\n",
      "torch.FloatTensor\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T07:41:00.222120Z",
     "start_time": "2025-11-04T07:41:00.203217Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 张量创建\n",
    "import torch\n",
    "\n",
    "a = torch.ones(3, 3)\n",
    "print(a)\n",
    "print(a.type())"
   ],
   "id": "b8db3d0b04aad25c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 1., 1.],\n",
      "        [1., 1., 1.],\n",
      "        [1., 1., 1.]])\n",
      "torch.FloatTensor\n"
     ]
    }
   ],
   "execution_count": 43
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T07:44:06.397823Z",
     "start_time": "2025-11-04T07:44:06.393535Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 建一个对角线都是1，其他全部都是0 的张量\n",
    "a = torch.eye(3,3)\n",
    "print(a)\n",
    "print(a.type())\n"
   ],
   "id": "e774075cd9c2d9a1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 0., 0.],\n",
      "        [0., 1., 0.],\n",
      "        [0., 0., 1.]])\n",
      "torch.FloatTensor\n"
     ]
    }
   ],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T07:49:20.433922Z",
     "start_time": "2025-11-04T07:49:20.429008Z"
    }
   },
   "cell_type": "code",
   "source": [
    "b = torch.Tensor(2,3)\n",
    "print(b)\n",
    "\n",
    "c = torch.zeros_like(b)\n",
    "print(c)\n",
    "print(c.type())\n",
    "\n",
    "d = torch.ones_like(a)\n",
    "print(d)\n",
    "print(d.to_dense())\n",
    "print(d.size())"
   ],
   "id": "c9a6688ed1344d9a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 0., 0.],\n",
      "        [0., 0., 0.]])\n",
      "tensor([[0., 0., 0.],\n",
      "        [0., 0., 0.]])\n",
      "torch.FloatTensor\n",
      "tensor([[1., 1., 1.],\n",
      "        [1., 1., 1.],\n",
      "        [1., 1., 1.]])\n",
      "tensor([[1., 1., 1.],\n",
      "        [1., 1., 1.],\n",
      "        [1., 1., 1.]])\n",
      "torch.Size([3, 3])\n"
     ]
    }
   ],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T07:47:21.990398Z",
     "start_time": "2025-11-04T07:47:21.945245Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 构造了5个随机数， 均值为0的满足正态分布的数据\n",
    "f = torch.normal(mean=0.0, std=torch.rand(5))\n",
    "print(f)\n",
    "print(f.size())\n",
    "\n"
   ],
   "id": "fe4c67f9f21592a3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 0.1698,  0.2782,  0.1752, -0.0088, -0.5541])\n",
      "torch.Size([5])\n"
     ]
    }
   ],
   "execution_count": 55
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T07:14:21.067561Z",
     "start_time": "2025-11-04T07:14:21.050906Z"
    }
   },
   "cell_type": "code",
   "source": "!set PYTORCH_CUDA_ALLOC_CONF='expandable_segments'",
   "id": "c6b4a86b76b1e7f4",
   "outputs": [],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T07:36:19.541704Z",
     "start_time": "2025-11-04T07:36:19.418670Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import gc\n",
    "\n",
    "# 检查GPU\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f\"使用设备: {device}\")\n",
    "\n",
    "def print_gpu_memory():\n",
    "    if torch.cuda.is_available():\n",
    "        allocated = torch.cuda.memory_allocated()/1024**3 # MB\n",
    "        total     = torch.cuda.get_device_properties(0).total_memory/1024**3\n",
    "        print(f'GPU内存: {allocated:.2f}GB/{total:.2f}GB ({allocated/total*100:.1f}%)')\n",
    "\n",
    "print(\"初始状态：\")\n",
    "print_gpu_memory()\n",
    "\n",
    "# 创建一系列逐渐增大的Tensor\n",
    "tensors = []\n",
    "sizes = [\n",
    "    (1000,1000),  #4MB\n",
    "    (2000,2000),  #16MB\n",
    "    (5000,1000),  #20MB\n",
    "    (3000,3000),  #36MB\n",
    "    (10000,5000),  #200MB\n",
    "    (10000,20000)  #200MB\n",
    "]\n",
    "for i, size in enumerate(sizes):\n",
    "    try:\n",
    "        tensor = torch.randn(*size, device=device)\n",
    "        tensors.append(tensor)\n",
    "        print(f\"\\n创建Tensor {size}:\")\n",
    "        print_gpu_memory()\n",
    "    except RuntimeError as e:\n",
    "        print(f\"创建Tensor {size} 时内存不足: {e}\")\n",
    "        break\n",
    "\n",
    "# 清理\n",
    "print(\"\\n 清理所有Tensor ...\")\n",
    "del tensors\n",
    "gc.collect()\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "print(\"最终状态\")\n",
    "print_gpu_memory()\n",
    "\n"
   ],
   "id": "be945c19bb2d10c9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cuda\n",
      "初始状态：\n",
      "GPU内存: 0.75GB/12.00GB (6.2%)\n",
      "\n",
      "创建Tensor (1000, 1000):\n",
      "GPU内存: 0.00GB/12.00GB (0.0%)\n",
      "\n",
      "创建Tensor (2000, 2000):\n",
      "GPU内存: 0.02GB/12.00GB (0.2%)\n",
      "\n",
      "创建Tensor (5000, 1000):\n",
      "GPU内存: 0.04GB/12.00GB (0.3%)\n",
      "\n",
      "创建Tensor (3000, 3000):\n",
      "GPU内存: 0.07GB/12.00GB (0.6%)\n",
      "\n",
      "创建Tensor (10000, 5000):\n",
      "GPU内存: 0.26GB/12.00GB (2.1%)\n",
      "\n",
      "创建Tensor (10000, 20000):\n",
      "GPU内存: 1.00GB/12.00GB (8.4%)\n",
      "\n",
      " 清理所有Tensor ...\n",
      "最终状态\n",
      "GPU内存: 0.75GB/12.00GB (6.2%)\n"
     ]
    }
   ],
   "execution_count": 42
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "e958f05489f4ad71"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-18T05:42:50.900793Z",
     "start_time": "2025-11-18T05:42:49.912423Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 张量的存储\n",
    "\n",
    "import torch\n",
    "\n",
    "points = torch.full([3,3], 6)\n",
    "\n",
    "\n",
    "print(points)"
   ],
   "id": "87d5b4b835e14d39",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[6, 6, 6],\n",
      "        [6, 6, 6],\n",
      "        [6, 6, 6]])\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-18T05:32:09.891961Z",
     "start_time": "2025-11-18T05:32:09.888330Z"
    }
   },
   "cell_type": "code",
   "source": "torch.save(points, './data/save/outpoints.pt')",
   "id": "c6bd858bd8a9c586",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-18T05:32:48.321794Z",
     "start_time": "2025-11-18T05:32:48.312955Z"
    }
   },
   "cell_type": "code",
   "source": [
    "with open('./data/save/outpoints.pt', 'rb') as f:\n",
    "    new_data = torch.load(f)\n",
    "\n",
    "new_data"
   ],
   "id": "99bfa96d6fc2f955",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[6, 6, 6],\n",
       "        [6, 6, 6],\n",
       "        [6, 6, 6]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-18T05:42:55.827638Z",
     "start_time": "2025-11-18T05:42:55.822380Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import  h5py\n",
    "\n",
    "points = torch.full([3,3], 6)\n",
    "print(points)\n",
    "\n",
    "f = h5py.File('./data/save/outpoints.h5', 'w')\n",
    "data = f.create_dataset('points', data=points.numpy())\n",
    "f.close()\n",
    "\n"
   ],
   "id": "af530806622899ce",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[6, 6, 6],\n",
      "        [6, 6, 6],\n",
      "        [6, 6, 6]])\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-18T05:43:22.304990Z",
     "start_time": "2025-11-18T05:43:22.272127Z"
    }
   },
   "cell_type": "code",
   "source": [
    "f = h5py.File('./data/save/outpoints.h5', 'r')\n",
    "\n",
    "dset = f['points']\n",
    "\n",
    "last_points = torch.from_numpy(dset[:])\n",
    "f.close()\n",
    "\n",
    "print(last_points)\n"
   ],
   "id": "8b4fa1e837f5fa7d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[6, 6, 6],\n",
      "        [6, 6, 6],\n",
      "        [6, 6, 6]])\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-18T05:36:05.317575Z",
     "start_time": "2025-11-18T05:33:52.608955Z"
    }
   },
   "cell_type": "code",
   "source": "! pip install h5py",
   "id": "9e445f4b18d01ecd",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting h5py\n",
      "  Downloading h5py-3.15.1-cp312-cp312-win_amd64.whl.metadata (3.1 kB)\n",
      "Requirement already satisfied: numpy>=1.21.2 in d:\\installed_tooles\\miniconda3\\envs\\yolotorch\\lib\\site-packages (from h5py) (2.3.2)\n",
      "Downloading h5py-3.15.1-cp312-cp312-win_amd64.whl (2.9 MB)\n",
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      "   ---------------------------------------- 2.9/2.9 MB 36.9 kB/s  0:02:06\n",
      "Installing collected packages: h5py\n",
      "Successfully installed h5py-3.15.1\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "ea537afc81954e03"
  }
 ],
 "metadata": {
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